Comparison Of Field Measurements Research Articles

Cooling performance study of a new cooling system in subway tunnel based on field measurement and CFD simulation

Abstract Some cities’ subways were constructed early and have been in operation for a long time. A large amount of heat accumulates in the rocks around the subway tunnels, causing the phenomenon of heat accumulation. This situation leads to the inadequate cooling capability of train air-conditioning systems, which, may even cease to function under extreme conditions. Currently, few solutions are available to address this issue. Therefore, this study proposes a new cooling system in subway tunnel. Considering the dusty environment inside the tunnel, the terminal equipment mainly consists of natural convection copper tube finless heat exchangers and a self-flushing device without fans, which cool using piston wind. By comparing field measurements of two tunnels with and without the cooling system in similar locations, the results show that the air temperature in the tunnels is reduced after the cooling system is installed. The results indicate that the average temperature in the tunnels decreases from 30.93 °C to 19.80 °C, marking a reduction of 11.13 °C after the cooling system runs for 24 hours. The temperature change in the tunnel is a long-term process, and actual measurements require significant time consumption. In this study, the long-term effect is predicted using CFD simulation in tunnels. The accuracy and credibility of the CFD simulation have been confirmed through its reasonable agreement with experimental data, with the final temperature after 24 hours achieving a relative error of less than 0.26%. Through the simulation, the temperature at a depth of 10 cm inside the tunnel wall after 24 hours is determined to be 27.56 °C, indicating a reduction of 3.44 °C compared to the initial temperature of 31 °C. This study can provide a reference for other subway tunnel cooling systems and serves as a basis for CFD simulations to verify cooling effects.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Flanking noise at stacked townhouse entrance stairs

In stacked, wood-framed townhouses, entry stairs for upper units pass through the footprint of lower units. The demising construction between the units, therefore, includes the stairs and floor-ceiling below, as well as the stairwell sidewalls. This irregular construction creates the possibility of significant sound flanking paths between upper and lower units, potentially leading to poor noise isolation performance and occupant dissatisfaction. This paper present practical solutions that have been used to address sound flanking in these areas, including the results of comparative field measurements to evaluate effectiveness. It is concluded that improvements are possible, although wood-framed entry stairs as typically designed are likely to remain an acoustical weakness.

Development and Optimization of a Chamber System Applied to Maize Net Ecosystem Carbon Exchange Measurements

Net ecosystem carbon exchange (NEE) in agricultural land represents a significant source of global greenhouse gas (GHG) emissions. While there are various tools for measuring NEE in agricultural fields, the chamber method remains the sole tool at the plot scale. In this research, we evaluated the NEE of maize plants at the nodulation stage using the flow-through chamber method. Many existing flow-through chamber systems directly introduce gases, leading to collisions with plants and subsequent turbulence inside the chamber. Turbulence can extend the time required to achieve a steady state. We modified the traditional flow-through chamber design to minimize turbulence in the measurement zone. Our modifications were validated by modeling the chamber and maize plants and by conducting fluid simulation experiments. In the analysis of our comparative field measurements between the two chamber designs, the use of the improved system notably shortened the time required to reach the steady state, increased the measurement frequency, and reduced the influence of changing environmental factors on the readings. Enhancing the measurement frequency is crucial for ensuring long-term accuracy. By reducing turbulence in the chamber, we anticipate improvements in the precision of NEE measurements in agricultural research, which could significantly contribute to an accurate assessment of the global carbon cycle.

Comparing field measurements and annotations as training data for UAV-based detection of standing dead trees in coniferous forest

ABSTRACT Deadwood provides a habitat for a large number of species and acts as a significant carbon storage. Thus, deadwood mapping allows planning various actions related to biodiversity conservation and forest management. Airborne laser scanning (ALS) provides an efficient means for monitoring large forested areas and is thus a potential method for deadwood mapping. Machine learning (ML) can be used for automating deadwood detection from ALS data. However, ML requires training data, collecting of which is laborious using field measurements. This study inspected the feasibility of training data manually annotated from aerial images for the mapping of individual standing dead trees. The standing dead trees were mapped from an ALS dataset collected with an unmanned aerial vehicle (UAV). The study was carried out by performing ML-based standing dead tree detection using annotated training data and field-measured training data, and comparing the performances of the models trained on these two datasets. The study found that using annotated training data improves the performance of standing dead tree detection due to its higher availability compared to field-measured data. For large trees (height > 14 m), the precision, recall, Cohen’s kappa score, and Matthews correlation coefficient achieved by the best classifier trained on annotated data were 0.23, 0.48, 0.17, and 0.20, respectively. In comparison, the corresponding metrics for the best classifier trained on field-measured data were 0.14, 0.52, 0.04, and 0.13. Annotated standing dead tree data is not a representative sample of the true standing dead tree population, as small trees and trees without crowns (snags) can often not be identified from aerial images. However, the study found that identifying such trees is challenging even when using field-measured training data, and thus using annotated training data does not bias results. In general, the results of the study showed that ALS-based standing dead tree detection should focus on the detection of large trees.

Investigating the thixotropic behaviour of Tremie Concrete using the Slump-flow test and the Material Point Method

This paper presents a new thixotropic model integrating the Papanastasiou–Bingham model with thixotropy equations to simulate the flow behaviour of Tremie Concrete in the Material Point Method framework. This study investigates the effect of thixotropy on the rheological behaviour of fresh concrete by comparing field measurements with numerical simulations. The comparison yields new insights into a critical and often overlooked behaviour of concrete. A parametric study is performed to understand the effect of model parameters and rest-time on the shear stress response of fresh concrete.

Wind loading on a stepped roof building: Comparison of field measurements, wind tunnel data, and standard provisions

This study investigates wind loads on stepped roof buildings measured in the field and in an atmospheric boundary layer wind tunnel to verify the suitability of the North American codes and standards for the design of stepped roofs. Good agreement has been generally found between field and wind tunnel results. The field measurements and wind tunnel results were compared with the NBCC 2020 and ASCE/SEI 7–22 (2022) provisions. It was found that the Edge provisions of NBCC 2020 are currently underestimated and need to be increased. The study suggests merging the Corner and Edge zones into a single “Perimeter” zone in NBCC 2020 as this would resolve the current underestimation of the NBCC Edge wind loads and add simplicity to the Code provisions. Peaks of ASCE/SEI 7–22 (2022) were found conservative, except for the zone near the step wall on the lower roof; ASCE/SEI 7–22 (2022) positive peaks seem to be underestimated.

Simulation of cemented paste backfill (CPB) deposition through column experiments: comparisons of field measurements, laboratory measurements, and analytical solutions

Estimating the as-placed properties of mine backfill is fundamental to optimizing the safety and productivity of underground mines. For cemented paste backfill (CPB), an important consideration is the extent to which self-weight consolidation during deposition may reduce the void ratio and enhance the binder’s effectiveness. Field monitoring and sampling campaigns can help investigate this phenomenon but they are expensive and logistically difficult. Therefore, mesoscale column experiments are performed in the controlled laboratory environment to better understand the coupling between self-weight consolidation and cement hydration. In this paper, columns are backfilled with uncemented paste tailings and with CPB and the pore water pressure, electrical conductivity, and volume changes are monitored during and after backfilling. The pore water pressure profiles at the end of backfilling are compared with an available analytical solution and restrictions on the solution’s validity are identified. Void ratios of samples taken from columns are compared with the ones obtained from field samples and the similarity indicates the filling conditions simulated in the laboratory are representative of field conditions. The changes in void ratio after curing were small compared to the initial void ratio of fresh CPB. The tests’ results help explain why self-weight consolidation during backfilling is not significant at the studied mine, which may be the case for many other mining operations as well.

Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)

Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500 m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e. fire-related forest loss, was modelled based on the overlap between 30 m forest loss data and MODIS burned area and active fire detections. Using this new 500 m model, we calculated global average carbon emissions from fire of 2.1±0.2 (±1σ interannual variability, IAV) Pg C yr−1 during 2002–2020. Fire-related forest loss accounted for 2.6±0.7 % (uncertainty range =1.9 %–3.3 %) of global burned area and 24±6 % (uncertainty range =16 %–31 %) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13±4 % of global emissions. Our global fire emissions estimate was higher than the 1.5 Pg C yr−1 from GFED4 and similar to 2.1 Pg C yr−1 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500 m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30 m and finer show a substantial amount of burned area that remains undetected with 500 m sensors, suggesting that global carbon emissions from fire are likely higher than our 500 m estimates. The ability to model fire emissions at 500 m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.

Increased Mass Loss of Glaciers in the Sawir Mountains of Central Asia between 1959 and 2021

Glacier mass balance can be regarded as a major direct index of climate variations. In this paper, a geodetic method was used to evaluate the mass balance of Sawir glaciers based on topographic map DEM (Digital Elevation Model), SRTM 30 m DEM, ASTER 30 m DEM, and Sentinel-1 Synthetic Aperture Radar 10 m DEM between 1959–2021, in order to explore the response to climatic alterations. In the case of Muz Taw glacier, the first comprehensive dataset concerning mass-balance readings for the 2014–2021 period was provided based on the eight-year consecutive field measurements. The glaciological average mass balance reached –883.4 ± 130 mm a–1 during this period. The geodetic mass balance for all glaciers of the Sawir Mountain range was −0.43 ± 0.12 m w. e. a−1 between 1959 and 2000, and accelerated to −0.56 ± 0.13 m w. e. a−1 between 2000 and 2021. A comparison of field measurements and remote-sensing approaches for determining the Muz Taw glacier’s mass balance between 2014–2021 proves the feasibility of the remote-sensing approach, which involves mass-balance monitoring based on DEMdata. In addition, our findings support the contention that air temperature is the dominant factor for accelerated glacier mass loss and surface elevation change.

On the integration of LiDAR and field data for riparian biomass estimation

The role of vegetation in supporting life on Earth is widely known. Nevertheless, the relevance of riparian corridors has been overlooked for a long time, leading to a dramatic reduction of vegetated buffers alongside them. Vegetation monitoring systems, including those for biomass estimation, are required to manage riparian corridors properly. Field surveys may support monitoring, but their usefulness is reduced by numerous drawbacks, therefore needing coupling with other data sources. The present work shows how Light Detection And Ranging (LiDAR) datasets can integrate targeted field measurements to estimate above-ground biomass at temperate or boreal latitudes and generate accurate biomass maps over large areas. By referring to the case study of the Orco river (northwest Italy), we defined a technique to reconstruct the geometry of an individual shrub from LiDAR point clouds. We tested the technique by comparing field measurements with Terrestrial and Airborne Laser Scanner data (TLS and ALS, respectively), assessing the former's superiority but the broader range of applicability of the latter. After these preliminary tests, we coupled the presented technique with a literature algorithm for individual tree detection, providing a more generalized procedure for the overall mapping and budgeting of riparian biomass based on ALS data. We applied the procedure to a fluvial bar of the Orco river, achieving a quantitative assessment of the shrub and tree biomass budget for 2019 and 2021 and visualizing the changes that occurred in that period. These results allowed us to shed light on the prevailing natural and anthropogenic processes in the investigated area and provide insights into the strengths and weaknesses of the proposed procedure.

Interpreting Sentinel-1 SAR Backscatter Signals of Snowpack Surface Melt/Freeze, Warming, and Ripening, through Field Measurements and Physically-Based SnowModel

The transition of a cold winter snowpack to one that is ripe and contributing to runoff is crucial to gauge for water resource management, but is highly variable in space and time. Snow surface melt/freeze cycles, associated with diurnal fluctuations in radiative inputs, are hallmarks of this transition. C-band synthetic aperture radar (SAR) reliably detects meltwater in the snowpack. Sentinel-1 (S1) C-band SAR offers consistent acquisition patterns that allow for diurnal investigations of melting snow. We used over 50 snow pit observations from 2020 in Grand Mesa, Colorado, USA, to track temperature and wetness in the snowpack as a function of depth and time during snowpack phases of warming, ripening, and runoff. We also ran the physically-based SnowModel, which provided a spatially and temporally continuous independent indication of snowpack conditions. Snowpack phases were identified and corroborated by comparing field measurements with SnowModel outputs. Knowledge of snowpack warming, ripening, and runoff phases was used to interpret diurnal changes in S1 backscatter values. Both field measurements and SnowModel simulations suggested that S1 SAR was not sensitive to the initial snowpack warming phase on Grand Mesa. In the ripening and runoff phases, the diurnal cycle in S1 SAR co-polarized backscatter was affected by both surface melt/freeze as well as the conditions of the snowpack underneath (ripening or ripe). The ripening phase was associated with significant increases in morning backscatter values, likely due to volume scattering from surface melt/freeze crusts, as well as significant decreases in evening backscatter values associated with snowmelt. During the runoff phase, both morning and evening backscatter decreased compared to reference values. These unique S1 diurnal signatures, and their interpretations using field measurements and SnowModel outputs, highlight the capacities and limitations of S1 SAR to understand snow surface states and bulk phases, which may offer runoff forecasting or energy balance model validation or parameterization, especially useful in remote or sparsely-gauged alpine basins.

Coevolution of the premaxillary ascending process and jaw protrusion in Lake Malawi cichlid fishes

Variation in jaw protrusion is critical to cichlid fish trophic diversification. For instance, jaw protrusion distance can influence suction, attack speed, as well as bite force, and jaw protrusion angle is associated with exploiting prey from different substrates. Interestingly, premaxillary ascending process length has been shown to influence the maximum distance some cichlid fishes protrude their oral jaws, but its relationship to jaw protrusion angle is unclear. Using phylogenetic comparative methods, morphometrics, and field measurements in Malawi cichlid species, we tested the relationship between the length of the premaxillary ascending process and two components of jaw protrusion. In Malawi, the premaxilla's ascending process length ranged from 6.9 to 15.2% with respect to standard length. Maximum jaw protrusion ranged from 1.4 to 9.1% of standard length and jaw protrusion angle varied from 17° to 76°. Although jaw protrusion angle was not associated with premaxillary ascending process length, phylogenetically adjusted correlations between the ascending process and maximum jaw protrusion distance were highly significant. Evolutionary change in the premaxilla is likely critical for determining the maximum distance, but not the angle, of Malawi cichlid jaw protrusion. Examinations of this type of potential phenotypic multi-functionality will continue to illuminate the mechanisms contributing to cichlid fish diversity.

Wind loading on a low-slope gabled roof: Comparison of field measurements, wind tunnel data, and code provisions

The current wind load provisions for low-slope roofs in the NBCC were established in the 70 s with wind tunnel experiments using analog data technology. These coefficients have remained almost unchanged. As the wind pressure measurement tools have significantly improved during the past 50 years, verification of the original pressure coefficients is required. The National Research Council of Canada (NRC), in collaboration with the Special Interest Group on Dynamic Evaluation of Roofing Systems (SIGDERS) consortium, conducted a wind load investigation to verify the suitability of the current wind load provisions. The investigation was carried out using both field measurements and wind tunnel simulations. An ideal full-scale representation of the low-slope roof specified in building code was instrumented and continuous data was collected for four years. A duplication of the building was evaluated by two wind tunnels to investigate the effect of critical wind directions. The paper presents the efforts made to make field measurements comparable to wind tunnel data and code provisions. It was observed that records with mean wind a speed above 22 mph (10 m/s) reduce the data variability and maximize reliability, which are important features for the code verification process. The goal of this research is to combine multiple sources of data to verify the suitability of wind load provisions on low-slope roofs. The research focuses on NBCC-2020 as an application. Evidence is presented that the NBCC-2020 provisions for the Roof Edge Zone is currently underestimated and needs to be increased. The study suggests taking the opportunity of Edge provision enhancement to merge Corner and Edge into a unique “Perimeter” load provision. The suggested code change addresses the current underestimation of the Edge Zone coefficients (wind load upgrade), and provides construction simplification to minimize roofing failure associated with labour-induced errors during cladding system installation (constructability upgrade).

Специфика использования ADCP в период ледостава

As a part of the modernization of the national hydrological network, a large number of ADCPs were delivered to the stream-gaging stations of the Roshydromet and, as a rule, used only during the open channel period. At the same time, the measurement of water discharge during the freeze-up period has always been a laborious, technically complex task, and using ADCPs during this period can optimize the measurement procedure. The article presents the issues of the features of ADCPs using on the national hydrological network of Roshydromet, the existing instrument and methodological base for measuring water discharge under the ice cover with ADCP. Either, the results of comparative field measurements of water discharge and velocities on the Ob, Neva and Oredezh rivers were considered. These rivers cover a wide range of hydrodynamic conditions: the Ob is a typical large river, with an average flow velocity is about 0.30 m/s, and a maximum depth about 20 m; the Neva is a unique large river, with a high average flow velocity value is about 0.8 m/s and a maximum depth not exceeding 11 m; the Oredezh river is a typical small river with mean flow velocity about 0.2 m/s and depths of not more than 1.5 m. In the experiments, ADCP Teledyne RiverRay and SonTek M9 were used, and comparative measurements were performed by the mechanical current meter and electromagnetic flow velocity device “Poseidon-1”. On the basis of these experiments first conclusions were drawn about the features of the ADCP application during the freeze-up period. Thus, the authors found that it is most effective to use ADCPs on medium and large rivers - labor and time costs are significantly lower than using conventional methods for measuring water flow, and the measurement accuracy is comparable. At the same time, the article notes a number of significant weak points, most of which boil down to the thesis that the measurement of water discharges using ADCP under ice involves the use of a large number of technically complex and expensive equipment, and the failure of at least one element leads to the inability to complete the measurement. Special attention is paid to the main technical aspects of working with devices, as well as with specialized software. Therefore, it is always recommended to have a spare standard measuring instrument in extra. The main technical aspects of working with devices as well as with specialized software are also discussed in more detail in the body of the article.

The Multi-Scale Layering-Structure of Thermal Microscale Profiles

Thermal microstructure profiling is an established technique for investigating turbulent mixing and stratification in lakes and oceans. However, it provides only quasi-instantaneous, 1-D snapshots. Other approaches to measuring these phenomena exist, but each has logistic and/or quality weaknesses. Hence, turbulent mixing and stratification processes remain greatly under-sampled. This paper contributes to addressing this problem by presenting a novel analysis of thermal microstructure profiles, focusing on their multi-scale stratification structure. Profiles taken in two small lakes using a Self-Contained Automated Micro-Profiler (SCAMP) were analysed. For each profile, buoyancy frequency (N), Thorpe scales (LT), and the coefficient of vertical turbulent diffusivity (KZ) were determined. To characterize the multi-scale stratification, profiles of d2T/dz2 at a spectrum of scales were calculated and the number of turning points in them counted. Plotting these counts against the scale gave pseudo-spectra, which were characterized by the index D of their power law regression lines. Scale-dependent correlations of D with N, LT and KZ were found, and suggest that this approach may be useful for providing alternative estimates of the efficiency of turbulent mixing and measures of longer-term averages of KZ than current methods provide. Testing these potential uses will require comparison of field measurements of D with time-integrated KZ values and numerical simulations.

Modelling carbon dioxide emissions under a maize-soy rotation using machine learning

Climatic parameters influence CO2 emissions and the complexity of the relationship is not fully captured in biophysical models. Machine learning (ML) is now being applied to environmental problems, and it is, therefore, opportune to investigate ML models in CO2 predictions from agricultural soils. In this study, six ML models were compared for their predictive performance by comparing field measurements of CO2 emissions from two fertiliser treatments: inorganic fertiliser (IF) and solid cattle manure supplemented with inorganic fertiliser (SCM) applied to a maize-soy rotation. The study also included a generalised scenario where all the data from IF and SCM were included in one dataset. The ML models include support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), feed-forward neural network (FNN), radial basis function neural network (RBFNN), and extreme learning machine (ELM). The input parameters were soil moisture, soil temperature, soil organic matter, soil total carbon, soil total nitrogen, air temperature, solar radiation and pan evaporation, while the output parameter was field measured CO2 emissions. The results of this study demonstrated that RF was the best at predicting CO2 emissions from IF [coefficient of determination (R2) = 0.92 and root mean square error (RMSE) = 2.27], SCM (R2 = 0.94 and RMSE = 2.86) and generalised scenarios (R2 = 0.86 and RMSE = 3.05). We conclude that ML models provide an innovative, robust and time-efficient alternative to biophysical models.

Predictability of initial hydrogeochemical effects induced by short-term infiltration of ∼75 °C hot water into a shallow glaciogenic aquifer

Despite their potential in heating supply systems, thus far high-temperature aquifer thermal energy storages (HT-ATES) currently lack widespread application. Reducing the potential risks by improving the predictability of hydrogeochemical processes accelerated or initiated at elevated temperatures might promote the development of this technology. Therefore, we report the results of a short-term hot water infiltration field test with subsurface temperatures above 70 °C, along with associated laboratory batch tests at 10, 40 and 70 °C for 28 sediment samples to determine their usability for geochemical prediction.Most groundwater components had lower maximal concentrations and smaller concentration ranges in field samples compared to the batch tests. This indicates that the strongest geochemical effects observed in laboratory tests with sufficient site-specific sediment samples will likely be attenuated at the field scale. A comparison of field measurements with predicted concentration ranges, based on temperature induced relative concentration changes from the batch tests, revealed that the predictive power was greatest, where the hot infiltrated water had cooled least and the strongest geochemical effects occurred. The batch test-based predictions showed the best accordance with field data for components, with significant temperature-induced concentration changes related to ion exchange and (de)sorption processes. However, accurate prediction of concentration changes based on other processes, e.g. mineral dissolution, and downstream reversals in concentrations, requires further investigation.The here presented procedure enables the prediction of maximal expectable temperature-dependant concentration changes for most environmentally relevant ancillary groundwater components, e.g. As, with limited effort.

Energy Efficient Approaches by Retrofitting Heat Pumps Water Heating System for a University Dormitory

With the cost of energy rising, the value of conservation grows. Interest in energy efficiency could be a sound investment or a necessary public policy. Heat pump systems provide economical alternatives of recovering heat from different sources for use in various applications. The objective of this study is to present the strategic approach on the energy efficient analysis of the water heating system retrofitted by applying a heat pump system in the dormitory of a university. Energy savings were determined by comparing field measurements of water consumption, water temperature and power consumption of the overall system before (electric resistance heating system) and after (heat pump heating system) the implementation of this project. Furthermore, the building energy simulation code (eQuest) has been applied to verify and predict the long-term energy consumption for both water heating systems. The results from energy modelling revealed the good agreement for energy simulation and field measurement data and the improvement of energy efficiency and energy savings could be achieved satisfactorily by retrofitting of a heat pump system. The energy conversion efficiency of hot water for energy consumption at 0.63 (Mcal/Mcal) could be achieved after the application of heat pump water heating system. It also presented the annual saving about USD 20,000 (NTD 600,000) for the dorm by using a heat pump heating system under the electrical billing rate of Taiwan.

A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock

Aiming at the rock-socketed pile in the soft rock area, this paper studies the inherent constitutive relationship between the vertical restraint stiffness at the pier bottom and the bearing capacity of the pile foundation. A new method to evaluate the bearing capacity of the pile foundation is proposed. Based on the Rayleigh energy method and the Southwell frequency synthesis method, the analytical expression of the vertical vibration fundamental frequency of the pier was calculated, and the constraint stiffness expression of the pier bottom was derived. By investigating the impact of parameters on the bearing capacity coefficient of the pile foundation, the fitting formula of the bearing capacity coefficient was obtained by multiple linear regression. Then, with this method, the vertical fundamental frequency of the pier was obtained through a field dynamic test to calculate the vertical constraint stiffness and evaluate the bearing capacity of the rock-socketed pile in the soft rock area. This method can overcome the shortcomings of the traditional static load test method, such as the high cost, long cycle, and poor representativeness. Finally, this method’s accuracy was verified by comparing field measurements and finite element simulation results. The results show that the difference between the code design constraint stiffness and the constraint stiffness by the frequency synthesis method was about 0.7%, and the bearing capacity difference between the analytical solution and the numerical simulation was small. The new method is accurate and effective.